Systems and methods for displaying autonomous vehicle environmental awareness

ABSTRACT

The disclosed computer-implemented method may include displaying vehicle environment awareness. In some embodiments, a visualization system may display an abstract representation of a vehicle&#39;s physical environment via a mobile device and/or a device embedded in the vehicle. For example, the visualization may use a voxel grid to represent the environment and may alter characteristics of shapes in the grid to increase their visual prominence when the sensors of the vehicle detect that an object is occupying the space represented by the shapes. In some embodiments, the visualization may gradually increase and reduce the visual prominence of shapes in the grid to create a soothing wave effect. Various other methods, systems, and computer-readable media are also disclosed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims the benefit of U.S.application Ser. No. 16/443,754, filed on 17 Jun. 2019, which is acontinuation of and claims the benefit of U.S. application Ser. No.16/132,327 (now U.S. Pat. No. 10,360,714), filed on Sep. 14, 2018, thedisclosure of which is incorporated, in its entirety, by this reference.

BACKGROUND

A dynamic transportation network that provides on-demand transportationto transportation requestors may include vehicles operated by humandrivers who participate in the dynamic transportation network as well asautonomous vehicles. Autonomous vehicles may safely and efficientlyprovide transportation to transportation requestors; however, thecapabilities and environmental awareness of an autonomous vehicle may beopaque to a requestor that rides in the autonomous vehicle. Accordingly,a graphical interface may provide a representation of an autonomousvehicle navigating within its environment to demonstrate theenvironmental awareness of the autonomous vehicle to the requestor,increasing requestor confidence in the autonomous vehicle.

However, attempting to provide a high-fidelity representation ofautonomous vehicle sensor data may translate poorly to human perception.In some examples, raw sensor data from an autonomous vehicle may not beformatted in a way that lends itself to producing accuratevisualizations. For example, a direct translation of raw data may causeobjects to appear to jitter unpredictably. In some examples, avisualization system attempting to precisely render a representation ofraw sensor data may incorrectly render harmless objects as othervehicles, creating the appearance of an imminent collision. Accordingly,the instant disclosure identifies and addresses a need for additionaland improved systems and methods for displaying autonomous vehicles'environmental awareness.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate a number of exemplary embodimentsand are a part of the specification. Together with the followingdescription, these drawings demonstrate and explain various principlesof the instant disclosure.

FIG. 1 illustrates an example scenario involving an autonomous vehicleand its surrounding environment as well as an example displayedrepresentation of that scenario.

FIG. 2 is an illustration of an example representation of an autonomousvehicle in context.

FIG. 3 is an illustration of an example representation of an autonomousvehicle and the physical environment surrounding the autonomous vehicle.

FIG. 4 is an illustration of an example representation of an autonomousvehicle and the physical environment surrounding the autonomous vehicle.

FIGS. 5A, 5B, and 5C are illustrations of an example representation ofan autonomous vehicle and the physical environment surrounding theautonomous vehicle.

FIG. 6 is an illustration of an example representation of an autonomousvehicle and the physical environment surrounding the autonomous vehicle.

FIG. 7 is an illustration of an example representation of an autonomousvehicle and the physical environment surrounding the autonomous vehicle.

FIG. 8 is an illustration of an example representation of an autonomousvehicle and the physical environment surrounding the autonomous vehicle.

FIG. 9 is a block diagram of an example system for displaying autonomousvehicle environmental awareness.

FIG. 10 is a flow diagram of an example method for displaying autonomousvehicle environmental awareness.

FIG. 11 is an illustration of an example requestor/provider managementenvironment.

FIG. 12 is an illustration of an example data collection and applicationmanagement system.

Throughout the drawings, identical reference characters and descriptionsindicate similar, but not necessarily identical, elements. While theexemplary embodiments described herein are susceptible to variousmodifications and alternative forms, specific embodiments have beenshown by way of example in the drawings and will be described in detailherein. However, the exemplary embodiments described herein are notintended to be limited to the particular forms disclosed. Rather, theinstant disclosure covers all modifications, equivalents, andalternatives falling within the scope of the appended claims.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

The present disclosure is generally directed to displaying vehicleenvironmental awareness by providing a passenger being transported by avehicle with a display that includes representations of the vehicleitself, the physical environment of the vehicle, and/or nearby objectsdetected by the vehicle's sensors. In some examples, the vehicle may bea non-autonomous vehicle that is equipped with environmental sensors toaid the driver. In other examples, the vehicle may be partially or fullyautonomous and may navigate based at least in part on data fromenvironmental sensors. In some examples, a user being transported by anautonomous vehicle may not have a high level of understanding of thevehicle's safety and/or capabilities. Displaying sensor data from thevehicle to the user may help the user to understand that the vehicle isaware of the surrounding environment and capable of safely transportingthe user to their destination. However, sensor data from an autonomousvehicle may be incomplete and/or poorly formatted for generating areal-time visualization. For example, the data may not be processed,normalized, and/or sanitized in a way that is conducive to efficientlyproducing an accurate visualization. In some cases, an autonomousvehicle vendor may be unable or unwilling to provide a meaningful data.For example, an autonomous vehicle may provide data that is the outputof one or more machine learning models and therefore is semanticallyuseful for autonomous vehicle decision-making in a machine learningcontext but is less semantically useful for creating a visualization. Inanother example, the data may be processed in different stages and thevisualization system may not receive fully processed data. In somecases, an autonomous vehicle vendor may provide a very large amount ofraw data, potentially overloading the processing capacity ofvisualization systems that only require a subset of the raw data.

Due to one or more of the above-described difficulties with transformingenvironmental data used by an autonomous vehicle into data useful forvisualization, visual anomalies may appear in visualization interfaces.For example, while an autonomous vehicle may correctly disregard abenign object floating past the autonomous vehicle (such as a plasticbag), a corresponding demonstration interface may incorrectly render thebenign object as another vehicle apparently about to crash into theautonomous vehicle. In another example, a visualization may displayanother vehicle as far closer to the autonomous vehicle than the othervehicle is in reality, making a collision appear more likely. In someexamples, a direct representation of raw sensor data and/or partiallyprocessed sensor data may cause surrounding environmental elements (suchas other vehicles) to jitter unpredictably, potentially causing theappearance of an imminent collision. Such inaccuracies may have theopposite of the intended effect as the appearance of imminent collisionsmay confuse the user as to what the autonomous vehicle's sensors arecurrently perceiving and the connection between the perception and thebehavior of the autonomous vehicle. Displaying a low-fidelity version ofthe vehicle's surroundings may convey enough information to the user toincrease the user's understanding of the data available to the vehiclewithout creating the possibility for catastrophic errors invisualization that may disturb the user.

Accordingly, as may be appreciated, the systems and methods describedherein may improve the functioning of a computer that facilitatestransportation via autonomous vehicles. In some embodiments, thecomputer may be part of an autonomous vehicle. Additionally oralternatively, the computer may be a mobile device. For example, thesesystems and methods may improve the functioning of the computer byimproving the user experience of a user who is using both the computerand an autonomous vehicle. Furthermore, for the reasons mentioned aboveand to be discussed in greater detail below, the systems and methodsdescribed herein may provide advantages to dynamic transportationmanagement and/or the field of transportation by increasing userwillingness to be transported by autonomous vehicles. In addition, thesesystems and methods may provide advantages to autonomous vehicles thatoperate as a part of a dynamic transportation network. For example, theautonomous vehicles may be rated as safer by users.

As will be explained in greater detail below, a dynamic transportationmatching system may arrange transportation on an on-demand and/or ad-hocbasis by, e.g., matching one or more transportation requestors and/ortransportation requestor devices with one or more transportationproviders and/or transportation provider devices. For example, a dynamictransportation matching system may match a transportation requestor to atransportation provider that operates within a dynamic transportationnetwork (e.g., that is managed by, coordinated by, and/or drawn from bythe dynamic transportation matching system to provide transportation totransportation requestors).

In some examples, available sources of transportation within a dynamictransportation network may include vehicles that are owned by an ownerand/or operator of the dynamic transportation matching system.Additionally or alternatively, sources of transportation within adynamic transportation network may include vehicles that are ownedoutside of the dynamic transportation network but that participatewithin the dynamic transportation network by agreement. In someexamples, the dynamic transportation network may include road-goingvehicles (e.g., cars, light trucks, etc.). Furthermore, the dynamictransportation network may include personal mobility vehicles. In someembodiments, a dynamic transportation network may include autonomousvehicles (e.g., self-driving cars) that may be capable of operating withlittle or no input from a human operator. In some examples, atransportation requestor may be matched with and/or transported by anautonomous vehicle with which the transportation requestor is notfamiliar and of which the transportation requestor may have a low levelof understanding.

FIG. 1 illustrates an example scenario involving an autonomous vehicleand its surrounding environment as well as an example displayedrepresentation of that scenario. In some examples, a display (e.g., ascreen) within an autonomous vehicle may display a rendering of theenvironment around the autonomous vehicle. For example, a displayedscenario 102 may show a displayed autonomous vehicle 106 as well as thesurroundings of displayed autonomous vehicle 106. In some examples, theautonomous vehicle may sense that an object is in front of the vehicleand a computing system may attempt to interpret the sensor data todetermine the type of object. In one example, the computing system maydisplay a displayed object 104, which may be a bus that appears to beabout to crash into the autonomous vehicle. In reality, thevisualization system may be inaccurately interpreting an actual scenario112, where an actual autonomous vehicle 110 may be about to harmlesslyrun over an actual object 114, such as a plastic bag in the roadway. Bydisplaying what appears to be a dangerous situation to the user, thevisualization may panic the user and/or reduce the user's understandingof whether autonomous vehicle is capable of accurately perceiving itssurroundings and/or behaving safely and effectively. For example, if theautonomous vehicle correctly identifies the object as harmless (despitethe error in the visualization) and continues on a straight path, theuser may be concerned that the autonomous vehicle continued straightinto something that appeared to be an oncoming bus and as a result, theuser may expect the autonomous vehicle to fail to avoid a collision withan actual bus. Additionally, a high-fidelity rendering of objects aroundthe autonomous vehicle may be jittery (e.g., show objects movingsuddenly at increments rather than smoothly) and may not accuratelyreflect reality or the data used by the autonomous vehicle to makedecisions, worsening the user experience.

FIG. 2 is an illustration of an example representation of an autonomousvehicle in context. As illustrated in FIG. 2, an autonomous vehicle 202may encounter an object 206, such as another vehicle. In some examples,a device 210 within autonomous vehicle 202 may display a representationof the autonomous vehicle, such as vehicle representation 208, and/or anabstracted representation of the object, such as object representation204. In some embodiments, device 210 may be a device associated with atransportation requestor being transported by autonomous vehicle 202,including but not limited to a smartphone, laptop, or tablet.Additionally or alternatively, device 210 may be a device associatedwith autonomous vehicle 202, such as a mobile device that is expected toremain within and/or with autonomous vehicle 202 (e.g., a tablet that isallocated to autonomous vehicle 202 by a dynamic transportation matchingsystem that manages autonomous vehicle 202) and/or a screen that isbuilt in to and/or otherwise affixed to autonomous vehicle 202. As willbe described in later detail below, in some embodiments, therepresentation of the environment and/or objects displayed on device 210may be a low-fidelity, smoothed-out, and/or otherwise abstracted versionof the sensor data received from autonomous vehicle 202. By displaying alow-fidelity version of the sensor data received from autonomous vehicle202 to a transportation requestor being transported by autonomousvehicle 202, the systems described herein may improve user understandingof the sensory data available to autonomous vehicle 202 and thereforeimprove user experience.

FIG. 3 is an illustration of an example representation of an autonomousvehicle and the physical environment surrounding the autonomous vehicle.As illustrated in FIG. 3, an environment representation 304 mayabstractly represent the physical environment around an autonomousvehicle via a grid of shapes (e.g., a voxel grid). In some embodiments,the grid may be a two-dimensional grid. Additionally or alternatively,the grid may be a three-dimensional grid. In one embodiments, the shapesmay be spheres. In some embodiments, if no nearby objects are present inthe physical environment, the shapes may be of a uniform size, color,and/or relative position. In some embodiments, the shapes may havedifferent sizes, colors, and/or physical positions to give a viewer asense of perspective. For example, shapes farther away from theautonomous vehicle and/or the camera angle of the view may be smaller bydefault. In some examples, shapes on the inside of the representation ofthe environment (near the autonomous vehicle) and/or on the outside ofthe representation of the environment (far from the autonomous vehicle)may have characteristics of lower intensity (e.g., color, size, etc.) toreduce the appearance of abruptness as objects enter or exit therepresentation of the environment.

In some embodiments, the representation may include a representation ofthe autonomous vehicle, such as autonomous vehicle representation 302,to act as a visual reference point. In some examples, the representationof the autonomous vehicle may be visually static. In other examples, therepresentation of the autonomous vehicle may change to represent changesto the vehicle, such as active brake lights and/or turn signals.

In some embodiments, the representation of the physical environment maybegin a certain distance away from the representation of the autonomousvehicle. In some examples, beginning the representation of the physicalenvironment a predetermined radius away from the representation of thevehicle may increase user comfort and/or ability to understand thevisualization by decreasing the appearance that the vehicle issurrounded by objects. Additionally or alternatively, in someembodiments, the representation of the physical environment may end acertain distance away from the representation of the autonomous vehicle.For example, the visualization may display a representation of thephysical environment in a torus that begins at a one-foot radius outsidethe autonomous vehicle and ends at a twenty-foot radius outside theautonomous vehicle. In some embodiments, the representation of thephysical environment may only display objects at certain heights. Forexample, the representation of the of the physical environment mayinclude objects that occupy space at or below the height of theautonomous vehicle, the height of a standard eighteen-wheeler truck,and/or the height of a standard traffic light. In one example, therepresentation of the physical environment may not display arepresentation of a bridge that the autonomous vehicle is passingunderneath despite the bridge being within the horizontal radius of thephysical environment represented in the visualization. By omittingrepresentations of objects that only occupy space above a certainheight, the visualization may avoid giving viewers the impression thatthe autonomous vehicle is about to crash into an object that theautonomous vehicle will in fact pass safely below.

FIG. 4 is an illustration of an example representation of an autonomousvehicle and the physical environment surrounding the autonomous vehicle.In some examples, the systems described herein may display, withinenvironment representation 404 around autonomous vehicle representation402, an object representation 406 of an object detected by theautonomous vehicle's sensors. In some examples, the systems describedherein may display object representation 406 in relation to autonomousvehicle representation 402 based on the position of the actual object inrelation to the actual vehicle.

In some embodiments, the systems described herein may display objectrepresentation 406 by altering the size, color, and/or position ofshapes in the grid that represents the physical environment. Forexample, the systems described herein may increase the size of a shapeto represent that an object occupies the position represented by theshape. Additionally or alternatively, the systems described herein mayalter the position of the shape (e.g., by moving the shape verticallyabove a horizontal grid) and/or the color of the shape (e.g., by shadinga shape from purple to pink). In some embodiments, the systems describedherein may incrementally alter a shape while the object occupies thespace represented by the shape. For example, if the object is within thespace represented by the shape for half a second, the systems describedherein may increase the size of the shape by ten percent. If the objectremains within the space represented by the shape for an additional halfsecond, the systems described herein may increase the size of the shapeby an additional ten percent. Similarly, the systems described hereinmay gradually change the color and/or position of the shape as theobject continues to occupy the space represented by the shape. In someembodiments, the systems described herein may continue incrementallymodifying the shape until a predetermined threshold is reached, such as200% of the original size of the shape, a set height above the grid,and/or a specific color. In some examples, the systems described hereinmay incrementally return the shape to its original settings after theobject leaves the shape represented by the shape. In some embodiments,the space represented by each shape may include a predetermined radiusaround the shape, such as radius 408 and/or radius 410. In one example,each sphere within a grid may represent a space of a radius that equalshalf the distance between itself and the adjacent spheres. In anotherexample, shapes may represent overlapping spaces.

FIGS. 5A, 5B, and 5C are illustrations of an example representation ofan autonomous vehicle and the physical environment surrounding theautonomous vehicle as an object moves through the space around thevehicle. In one example, the systems described herein may display anenvironment representation 504 around an autonomous vehiclerepresentation 502. In one example, as an object enters the space aroundthe vehicle, the systems described herein may display an objectrepresentation 506(a) by altering characteristics of shapes at the edgeof a grid that makes up environment representation 504. As the objectdraws alongside the vehicle, the systems described herein may display anobject representation 506(b) by returning the shapes at the edge of thegrid to their original characteristics while altering the shapes thatrepresent the space currently occupied by the object. As the objectmoves away from the vehicle, the systems described herein may display anobject representation 506(c). In some examples, by gradually alteringthe size, color, and/or position of spheres in a grid as the objectmoves, the systems described herein may create a smoothing and/orwave-like effect that smoothly displays the movement of the object(and/or the movement of the vehicle relative to the object) without thejitteriness that is sometimes the byproduct of reproducingvisualizations directly from sensor data.

FIG. 6 is an illustration of an example representation of an autonomousvehicle and the physical environment surrounding the autonomous vehicle.In some embodiments, if an interruption in sensor data received from theautonomous vehicle occurs, the systems described herein may update thevisualization to reflect the interruption. For example, as illustratedin FIG. 6, the systems described herein may continue to displayautonomous vehicle representation 602 as normal but may alterenvironment representation 604. In one example, the systems describedherein may change the color of the shapes that make up environmentrepresentation 604, for example, by turning all of the shapes grey. Inanother example, the systems described herein may alter the size of theshapes, for example, by shrinking the shapes. In another example, thesystems described herein may entirely remove the representation of thephysical environment from the visualization. In some embodiments, thesystems described herein may notify the user via a textual message,within the visualization and/or out-of-band, that states that there hasbeen a temporary interruption in sensor data to the visualization.

In some embodiments, the systems described herein may change thevisualization to reflect that a human operator has taken control of theautonomous vehicle. In some examples, the systems described herein mayalter and/or stop displaying the representation of the physicalenvironment. Additionally or alternatively, the systems described hereinmay stop displaying one or more representations of the predictedbehavior of the autonomous vehicle. For example, the systems describedherein may calculate a predicted path of the autonomous vehicle based ondata received from the autonomous vehicle (e.g., acceleration,deceleration, steering, and/or navigation system data) and may displaythe predicted path in the visualization. In some embodiments, when ahuman operator takes control of the autonomous vehicle, the systemsdescribed herein may cease displaying the predicted path of the vehicle.

FIG. 7 is an illustration of an example representation of an autonomousvehicle, the physical environment surrounding the autonomous vehicle,and additional data. In some embodiments, the systems described hereinmay display additional data 708 alongside autonomous vehiclerepresentation 702 and/or environmental representation 704. In someembodiments, the systems described herein may display additional datawithin a visualization including but not limited to the current speed ofthe vehicle, the current fuel economy of the vehicle, the distancetraveled this trip, the remaining distance this trip, and/or thedistance to and/or from a transfer point. In some examples, the systemsdescribed herein may display icons and/or text representing trafficcontrol devices encountered by the autonomous vehicle such as trafficlights, stop signs, yield signs, and/or other street signs. In oneembodiment, the systems described herein may calculate a predicted pathof the autonomous vehicle based on data received from the autonomousvehicle and may display predicted path 706 as part of the visualization.In one embodiment, the display of predicted path 706 may vary based onthe confidence in the accuracy of predicted path 706 based on datareceived from the autonomous vehicle. In some embodiments, predictedpath 706 may be opaque when the autonomous vehicle is moving andtransparent when the autonomous vehicle is not moving and/or may changein opacity based on the speed of the autonomous vehicle. In someexamples, if the autonomous vehicle is expected to change directiondrastically and/or is currently changing direction drastically,predicted path 706 may be shown with dotted rather than solid lines. Insome embodiments, the systems described herein may generate predictedpath 706 based on data gathered from an autonomous path planner thatplots the trajectory of the vehicle in a future time period and/or aroute indicator that has access to information about the destination ofthe vehicle.

FIG. 8 is an illustration of an example representation of an autonomousvehicle and the physical environment surrounding the autonomous vehicle.In some embodiments, the systems described herein may showrepresentations of objects in the visualization even if those objectsare outside of the representation of the environment. For example, thesystems described herein may display object representation 806 despiteobject representation 806 representing an object occupying space outsideof the space represented by environment representation 804 aroundautonomous vehicle representation 802. In some embodiments, the systemsdescribed herein may only display objects outside of the representationof the environment under special circumstances, such as if the object isabove and/or below a certain size, moving at a certain speed, and/orotherwise indicated to be an object of interest (e.g., by the sensors ofthe autonomous vehicle). In other embodiments, the systems describedherein may display any object detected by the autonomous vehicle'ssensors whether or not the object is within the radius covered by therepresentation of the physical environment.

In some embodiments, the systems described herein may provide atransportation requestor being transported by an autonomous vehicle withinformation about the autonomous vehicle and/or the surroundingenvironment of the autonomous vehicle via means other than a display.For example, the systems described herein may provide haptic feedback(e.g., vibration) to indicate the presence and/or location of nearbyobjects in the physical environment. Additionally or alternatively, thesystems described herein may provide audio information about nearbyobjects. For example, the systems described herein may play a tone toindicate that an object has been detected by the sensors of theautonomous vehicle. In one embodiment, the systems described herein mayplay a tone from a speaker that corresponds to a position of the object(e.g., from a rear speaker if the object is behind the vehicle).

FIG. 9 illustrates an example system 900 for matching transportationrequests with a dynamic transportation network that includes personalmobility vehicles. As shown in FIG. 9, a dynamic transportation matchingsystem 910 may be configured with one or more dynamic transportationmatching modules 912 that may perform one or more of the steps describedherein. Dynamic transportation matching system 910 may represent anycomputing system and/or set of computing systems capable of matchingtransportation requests. Dynamic transportation matching system 910 maybe in communication with computing devices in each of a group ofvehicles 920. Vehicles 920 may represent any vehicles that may fulfilltransportation requests. In some examples, vehicles 920 may includedisparate vehicle types and/or models. For example, vehicles 920 mayinclude road-going vehicles and personal mobility vehicles. In someexamples, some of vehicles 920 may be standard commercially availablevehicles. According to some examples, some of vehicles 920 may be ownedby separate individuals (e.g., transportation providers). Furthermore,while, in some examples, many or all of vehicles 920 may behuman-operated, in some examples many of vehicles 920 may also beautonomous (or partly autonomous). Accordingly, throughout the instantdisclosure, references to a “transportation provider” (or “provider”)may, where appropriate, refer to an operator of a human driven vehicle,an autonomous vehicle control system, an autonomous vehicle, an owner ofan autonomous vehicle, an operator of an autonomous vehicle, anattendant of an autonomous vehicle, a vehicle piloted by a requestor,and/or an autonomous system for piloting a vehicle. While FIG. 2 doesnot specify the number of vehicles 920, it may be readily appreciatedthat the systems described herein are applicable to hundreds ofvehicles, thousands of vehicles, or more. In one example, dynamictransportation matching system 910 may coordinate transportationmatchings within a single region for 50,000 vehicles or more on a givenday. In some examples, vehicles 920 may collectively form a dynamictransportation network that may provide transportation supply on anon-demand basis to transportation requestors.

As mentioned above, dynamic transportation matching system 910 maycommunicate with computing devices in each of vehicles 920. Thecomputing devices may be any suitable type of computing device. In someexamples, one or more of the computing devices may be integrated intothe respective vehicles 920. In some examples, one or more of thecomputing devices may be mobile devices. For example, one or more of thecomputing devices may be smartphones. Additionally or alternatively, oneor more of the computing devices may be tablet computers, personaldigital assistants, or any other type or form of mobile computingdevice. According to some examples, one or more of the computing devicesmay include wearable computing devices (e.g., a driver-wearablecomputing device), such as smart glasses, smart watches, etc. In someexamples, one or more of the computing devices may be devices suitablefor temporarily mounting in a vehicle (e.g., for use by a requestorand/or provider for a transportation matching application, a navigationapplication, and/or any other application suited for the use ofrequestors and/or providers). Additionally or alternatively, one or moreof the computing devices may be devices suitable for installing in avehicle and/or may be a vehicle's computer that has a transportationmanagement system application installed on the computer in order toprovide transportation services to transportation requestors and/orcommunicate with dynamic transportation matching system 910.

As shown in FIG. 9, vehicles 920 may include provider devices 930(1)-(n)(e.g., whether integrated into the vehicle, permanently affixed to thevehicle, temporarily affixed to the vehicle, worn by a driver of thevehicle, etc.). In some examples, provider devices 930 may include aprovider app 940. Provider app 940 may represent any application,program, and/or module that may provide one or more services related tooperating a vehicle and/or providing transportation matching services.For example, provider app 940 may include a transportation matchingapplication for providers. In some examples, provider application 940may match the user of provider app 940 (e.g., a transportation provider)with transportation requestors through communication with dynamictransportation matching system 910. In addition, and as is described ingreater detail below, provider app 940 may provide dynamictransportation management system 910 with information about a provider(including, e.g., the current location of the provider) to enabledynamic transportation management system 910 to provide dynamictransportation matching and/or management services for the provider andone or more requestors. In some examples, provider app 940 maycoordinate communications and/or a payment between a requestor and aprovider. According to some embodiments, provider app 940 may provide amap service, a navigation service, a traffic notification service,and/or a geolocation service.

Additionally, as shown in FIG. 9, dynamic transportation matching system910 may communicate with requestor devices 950( 1 )-(m). In someexamples, requestor devices 950 may include a requestor app 960.Requestor app 960 may represent any application, program, and/or modulethat may provide one or more services related to requestingtransportation matching services. For example, requestor app 960 mayinclude a transportation matching application for requestors. In someexamples, requestor app 960 may match the user of requestor app 960(e.g., a transportation requestor) with transportation providers throughcommunication with dynamic transportation matching system 910. Inaddition, and as is described in greater detail below, requestor app 960may provide dynamic transportation management system 910 withinformation about a requestor (including, e.g., the current location ofthe requestor) to enable dynamic transportation management system 910 toprovide dynamic transportation matching services for the requestor andone or more providers. In some examples, requestor app 960 maycoordinate communications and/or a payment between a requestor and aprovider. According to some embodiments, requestor app 960 may provide amap service, a navigation service, a traffic notification service,and/or a geolocation service.

Embodiments of the instant disclosure may include or be implemented inconjunction with a dynamic transportation matching system. Atransportation matching system may arrange transportation on anon-demand and/or ad-hoc basis by, e.g., matching one or moretransportation requestors with one or more transportation providers. Forexample, a transportation matching system may provide one or moretransportation matching services for a ridesharing service, aridesourcing service, a taxicab service, a car-booking service, anautonomous vehicle service, a personal mobility vehicle service, or somecombination and/or derivative thereof. The transportation matchingsystem may include and/or interface with any of a variety of subsystemsthat may implement, support, and/or improve a transportation matchingservice. For example, the transportation matching system may include amatching system (e.g., that matches requestors to ride opportunitiesand/or that arranges for requestors and/or providers to meet), a mappingsystem, a navigation system (e.g., to help a provider reach a requestor,to help a requestor reach a provider, and/or to help a provider reach adestination), a reputation system (e.g., to rate and/or gauge thetrustworthiness of a requestor and/or a provider), a payment system,and/or an autonomous or semi-autonomous driving system. Thetransportation matching system may be implemented on various platforms,including a requestor-owned mobile device, a computing system installedin a vehicle, a requestor-owned mobile device, a server computer system,or any other hardware platform capable of providing transportationmatching services to one or more requestors and/or providers.

FIG. 10 illustrates an example method 1000 for determining allocation ofpersonal mobility vehicles. As illustrated in FIG. 10, at step 1010, oneor more of the systems described herein may display, via a display in avehicle, a visualization of the vehicle, where the visualization mayinclude a representation of the vehicle and a representation of aphysical environment of the vehicle.

In some examples, the systems described herein may display therepresentation of the physical environment of the vehicle by displayingthe representation of the physical environment within a limited radiusaround the vehicle. Additionally or alternatively, the systems describedherein may display the representation of the physical environment of thevehicle by displaying the representation of the physical environmentstarting beyond a predetermined radius around the vehicle. In oneembodiment, the systems described herein may determine, based on thesensor data, a position of an object outside the limited radius aroundthe vehicle and may display a representation of the object within thevisualization outside the representation of the physical environment

In one embodiment, the systems described herein may display thevisualization by sending the visualization to a mobile computing deviceassociated with a passenger being transported within the vehicle.Additionally or alternatively, the systems described herein may displaythe visualization by sending the visualization to a display deviceassociated with the vehicle.

In one embodiment, the vehicle may be an autonomous vehicle. In someembodiments, displaying the visualization may include predicting, basedon data received from at least one system of the vehicle autonomous, aprojected direction of travel of the autonomous vehicle and displaying arepresentation of the projected direction of travel within thevisualization.

At step 1020, one or more of the systems described herein may receive,from at least one sensor of the vehicle, sensor data associated with thephysical environment.

In one embodiment, the systems described herein may receive the sensordata associated with the physical environment by receiving incompletesensor data from a third-party source that does not include completesensor data perceived by sensors of the vehicle. Additionally oralternatively, the systems described herein may receive the sensor dataassociated with the physical environment by receiving sensor data from athird-party source that is not formatted for producing the visualizationand may extract, from the sensor data from the third-party source thatis not formatted for producing the visualization, relevant data toproduce the visualization. In one example, receiving the sensor dataassociated with the physical environment may include receiving an objectbounding box associated with the object.

At step 1030, one or more of the systems described herein may determine,based on the sensor data received from the vehicle, a position of atleast one object in the physical environment relative to the vehicle.

At step 1040, one or more of the systems described herein may apply, tothe sensor data received from the vehicle, a transformation that reducesa precision of the sensor data to create a smoothing effect in thevisualization that reduces an abruptness of an appearance of arepresentation of the position of the object.

At step 1050, one or more of the systems described herein may update,using the smoothing effect, the visualization to display arepresentation of the position of the object within the representationof the physical environment.

In one embodiment, the visualization may include a grid of shapes thatrepresent the physical environment, applying the transformation thatreduces the precision of the sensor data to create the smoothing effectin the visualization may include mapping the position of the object toat least one shape in the grid of shapes, and updating the visualizationto display the representation of the position of the object may includemodifying at least one attribute of at least one shape in the grid ofshapes that represents the position of the object. In some examples, thesystems described herein may modify the attribute of the shape byincrementally modifying the attribute of the shape based at least inpart on a length of time that the object occupies the position relativeto the vehicle.

In one embodiment, the systems described herein may apply, to the sensordata received from the vehicle, the transformation that reduces theprecision of the sensor data to create the smoothing effect in thevisualization by specifying the grid of shape to represent alow-fidelity model of the physical environment that does not capture ahigh level of precision. In some examples, the systems described hereinmay modify the attribute of the at least one shape in the grid of shapesthat represents the position of the object by modifying the color of theshape. In some examples, the systems described herein may modify theattribute of the at least one shape in the grid of shapes thatrepresents the position of the object by modifying the size of theshape. Additionally or alternatively, the systems described herein maymodify the attribute of the at least one shape in the grid of shapesthat represents the position of the object by modifying the position ofthe shape.

In one embodiment, the systems described herein may detect that a humandriver has assumed control of the autonomous vehicle and may update thevisualization to reflect that the human driver has assumed control ofthe autonomous vehicle. In some examples, the systems described hereinmay update the visualization to reflect that the human driver hasassumed control of the autonomous vehicle by ceasing to display at leastone element within the visualization that represents a prediction of abehavior of the autonomous vehicle based on the sensor data.

FIG. 11 shows a transportation management environment 1100, inaccordance with various embodiments. As shown in FIG. 11, atransportation management system 1102 may run one or more servicesand/or software applications, including identity management services1104, location services 1106, ride services 1108, and/or other services.Although FIG. 11 shows a certain number of services provided bytransportation management system 1102, more or fewer services may beprovided in various implementations. In addition, although FIG. 11 showsthese services as being provided by transportation management system1102, all or a portion of any of the services may be processed in adistributed fashion. For example, computations associated with a servicetask may be performed by a combination of transportation managementsystem 1102 (including any number of servers, databases, etc.), one ormore devices associated with a provider (e.g., devices integrated withmanaged vehicles 1114(a), 1114(b), and/or 1114(c); provider computingdevices 1116 and tablets 1120; and transportation management vehicledevices 1118), and/or more or more devices associated with a riderequestor (e.g., the requestor's computing devices 1124 and tablets1122). In some embodiments, transportation management system 1102 mayinclude one or more general purpose computers, server computers,clustered computing systems, cloud-based computing systems, and/or anyother computing systems or arrangements of computing systems.Transportation management system 1102 may be configured to run any orall of the services and/or software components described herein. In someembodiments, the transportation management system 1102 may include anappropriate operating system and/or various server applications, such asweb servers capable of handling hypertext transport protocol (HTTP)requests, file transfer protocol (FTP) servers, database servers, etc.

In some embodiments, identity management services 1104 may be configuredto perform authorization services for requestors and providers and/ormanage their interactions and/or data with transportation managementsystem 1102. This may include, e.g., authenticating the identity ofproviders and determining that they are authorized to provide servicesthrough transportation management system 1102. Similarly, requestors'identities may be authenticated to determine whether they are authorizedto receive the requested services through transportation managementsystem 1102. Identity management services 1104 may also manage and/orcontrol access to provider and/or requestor data maintained bytransportation management system 1102, such as driving and/or ridehistories, vehicle data, personal data, preferences, usage patterns as aride provider and/or as a ride requestor, profile pictures, linkedthird-party accounts (e.g., credentials for music and/or entertainmentservices, social-networking systems, calendar systems, task-managementsystems, etc.) and any other associated information. Transportationmanagement system 1102 may also manage and/or control access to providerand/or requestor data stored with and/or obtained from third-partysystems. For example, a requester or provider may grant transportationmanagement system 1102 access to a third-party email, calendar, or taskmanagement system (e.g., via the user's credentials). As anotherexample, a requestor or provider may grant, through a mobile device(e.g., 1116, 1120, 1122, or 1124), a transportation applicationassociated with transportation management system 1102 access to dataprovided by other applications installed on the mobile device. In someexamples, such data may be processed on the client and/or uploaded totransportation management system 1102 for processing.

In some embodiments, transportation management system 1102 may provideride services 1108, which may include ride matching and/or managementservices to connect a requestor to a provider. For example, afteridentity management services module 1104 has authenticated the identitya ride requestor, ride services module 1108 may attempt to match therequestor with one or more ride providers. In some embodiments, rideservices module 1108 may identify an appropriate provider using locationdata obtained from location services module 1106. Ride services module1108 may use the location data to identify providers who aregeographically close to the requestor (e.g., within a certain thresholddistance or travel time) and/or who are otherwise a good match with therequestor. Ride services module 1108 may implement matching algorithmsthat score providers based on, e.g., preferences of providers andrequestors; vehicle features, amenities, condition, and/or status;providers' preferred general travel direction and/or route, range oftravel, and/or availability; requestors' origination and destinationlocations, time constraints, and/or vehicle feature needs; and any otherpertinent information for matching requestors with providers. In someembodiments, ride services module 1108 may use rule-based algorithmsand/or machine-learning models for matching requestors and providers.

Transportation management system 1102 may communicatively connect tovarious devices through networks 1110 and/or 1112. Networks 1110 and1112 may include any combination of interconnected networks configuredto send and/or receive data communications using various communicationprotocols and transmission technologies. In some embodiments, networks1110 and/or 1112 may include local area networks (LANs), wide-areanetworks (WANs), and/or the Internet, and may support communicationprotocols such as transmission control protocol/Internet protocol(TCP/IP), Internet packet exchange (IPX), systems network architecture(SNA), and/or any other suitable network protocols. In some embodiments,data may be transmitted through networks 1110 and/or 1112 using a mobilenetwork (such as a mobile telephone network, cellular network, satellitenetwork, or other mobile network), a public switched telephone network(PSTN), wired communication protocols (e.g., Universal Serial Bus (USB),Controller Area Network (CAN)), and/or wireless communication protocols(e.g., wireless LAN (WLAN) technologies implementing the IEEE 902.11family of standards, Bluetooth, Bluetooth Low Energy, Near FieldCommunication (NFC), Z-Wave, and ZigBee). In various embodiments,networks 1110 and/or 1112 may include any combination of networksdescribed herein or any other type of network capable of facilitatingcommunication across networks 1110 and/or 1112.

In some embodiments, transportation management vehicle device 1118 mayinclude a provider communication device configured to communicate withusers, such as drivers, passengers, pedestrians, and/or other users. Insome embodiments, transportation management vehicle device 1118 maycommunicate directly with transportation management system 1102 orthrough another provider computing device, such as provider computingdevice 1116. In some embodiments, a requestor computing device (e.g.,device 1124) may communicate via a connection 1126 directly withtransportation management vehicle device 1118 via a communicationchannel and/or connection, such as a peer-to-peer connection, Bluetoothconnection, NFC connection, ad hoc wireless network, and/or any othercommunication channel or connection. Although FIG. 11 shows particulardevices communicating with transportation management system 1102 overnetworks 1110 and 1112, in various embodiments, transportationmanagement system 1102 may expose an interface, such as an applicationprogramming interface (API) or service provider interface (SPI) toenable various third parties which may serve as an intermediary betweenend users and transportation management system 1102.

In some embodiments, devices within a vehicle may be interconnected. Forexample, any combination of the following may be communicativelyconnected: vehicle 1114, provider computing device 1116, provider tablet1120, transportation management vehicle device 1118, requestor computingdevice 1124, requestor tablet 1122, and any other device (e.g., smartwatch, smart tags, etc.). For example, transportation management vehicledevice 1118 may be communicatively connected to provider computingdevice 1116 and/or requestor computing device 1124. Transportationmanagement vehicle device 1118 may establish communicative connections,such as connections 1126 and 1128, to those devices via any suitablecommunication technology, including, e.g., WLAN technologiesimplementing the IEEE 902.11 family of standards, Bluetooth, BluetoothLow Energy, NFC, Z-Wave, ZigBee, and any other suitable short-rangewireless communication technology.

In some embodiments, users may utilize and interface with one or moreservices provided by the transportation management system 1102 usingapplications executing on their respective computing devices (e.g.,1116, 1118, 1120, and/or a computing device integrated within vehicle1114), which may include mobile devices (e.g., an iPhone®, an iPad®,mobile telephone, tablet computer, a personal digital assistant (PDA)),laptops, wearable devices (e.g., smart watch, smart glasses, headmounted displays, etc.), thin client devices, gaming consoles, and anyother computing devices. In some embodiments, vehicle 1114 may include avehicle-integrated computing device, such as a vehicle navigationsystem, or other computing device integrated with the vehicle itself,such as the management system of an autonomous vehicle. The computingdevice may run on any suitable operating systems, such as Android®,iOS®, macOS®, Windows®, Linux®, UNIX®, or UNIX®-based or Linux®-basedoperating systems, or other operating systems. The computing device mayfurther be configured to send and receive data over the Internet, shortmessage service (SMS), email, and various other messaging applicationsand/or communication protocols. In some embodiments, one or moresoftware applications may be installed on the computing device of aprovider or requestor, including an application associated withtransportation management system 1102. The transportation applicationmay, for example, be distributed by an entity associated with thetransportation management system via any distribution channel, such asan online source from which applications may be downloaded. Additionalthird-party applications unassociated with the transportation managementsystem may also be installed on the computing device. In someembodiments, the transportation application may communicate or sharedata and resources with one or more of the installed third-partyapplications.

FIG. 12 shows a data collection and application management environment1200, in accordance with various embodiments. As shown in FIG. 12,management system 1202 may be configured to collect data from variousdata collection devices 1204 through a data collection interface 1206.As discussed above, management system 1202 may include one or morecomputers and/or servers or any combination thereof. Data collectiondevices 1204 may include, but are not limited to, user devices(including provider and requestor computing devices, such as thosediscussed above), provider communication devices, laptop or desktopcomputers, vehicle data (e.g., from sensors integrated into or otherwiseconnected to vehicles), ground-based or satellite-based sources (e.g.,location data, traffic data, weather data, etc.), or other sensor data(e.g., roadway embedded sensors, traffic sensors, etc.). Data collectioninterface 1206 can include, e.g., an extensible device frameworkconfigured to support interfaces for each data collection device. Invarious embodiments, data collection interface 1206 may be extended tosupport new data collection devices as they are released and/or toupdate existing interfaces to support changes to existing datacollection devices. In various embodiments, data collection devices maycommunicate with data collection interface 1206 over one or morenetworks. The networks may include any network or communication protocolas would be recognized by one of ordinary skill in the art, includingthose networks discussed above.

As shown in FIG. 12, data received from data collection devices 1204 canbe stored in data store 1208. Data store 1208 may include one or moredata stores, such as databases, object storage systems and services,cloud-based storage services, and other data stores. For example,various data stores may be implemented on a non-transitory storagemedium accessible to management system 1202, such as historical datastore 1210, ride data store 1212, and user data store 1214. Data stores1208 can be local to management system 1202, or remote and accessibleover a network, such as those networks discussed above or a storage-areanetwork or other networked storage system. In various embodiments,historical data 1210 may include historical traffic data, weather data,request data, road condition data, or any other data for a given regionor regions received from various data collection devices. Ride data 1212may include route data, request data, timing data, and other riderelated data, in aggregate and/or by requestor or provider. User data1214 may include user account data, preferences, location history, andother user-specific data. Although certain data stores are shown by wayof example, any data collected and/or stored according to the variousembodiments described herein may be stored in data stores 1208.

As shown in FIG. 12, an application interface 1216 can be provided bymanagement system 1202 to enable various apps 1218 to access data and/orservices available through management system 1202. Apps 1218 may run onvarious user devices (including provider and requestor computingdevices, such as those discussed above) and/or may include cloud-basedor other distributed apps configured to run across various devices(e.g., computers, servers, or combinations thereof). Apps 1218 mayinclude, e.g., aggregation and/or reporting apps which may utilize data1208 to provide various services (e.g., third-party ride request andmanagement apps). In various embodiments, application interface 1216 caninclude an API and/or SPI enabling third party development of apps 1218.In some embodiments, application interface 1216 may include a webinterface, enabling web-based access to data 1208 and/or servicesprovided by management system 1202. In various embodiments, apps 1218may run on devices configured to communicate with application interface1216 over one or more networks. The networks may include any network orcommunication protocol as would be recognized by one of ordinary skillin the art, including those networks discussed above, in accordance withan embodiment of the present disclosure.

While various embodiments of the present disclosure are described interms of a ridesharing service in which the ride providers are humandrivers operating their own vehicles, in other embodiments, thetechniques described herein may also be used in environments in whichride requests are fulfilled using autonomous vehicles. For example, atransportation management system of a ridesharing service may facilitatethe fulfillment of ride requests using both human drivers and autonomousvehicles.

As detailed above, the computing devices and systems described and/orillustrated herein broadly represent any type or form of computingdevice or system capable of executing computer-readable instructions,such as those contained within the modules described herein. In theirmost basic configuration, these computing device(s) may each include atleast one memory device and at least one physical processor.

In some examples, the term “memory device” generally refers to any typeor form of volatile or non-volatile storage device or medium capable ofstoring data and/or computer-readable instructions. In one example, amemory device may store, load, and/or maintain one or more of themodules described herein. Examples of memory devices include, withoutlimitation, Random Access Memory (RAM), Read Only Memory (ROM), flashmemory, Hard Disk Drives (HDDs), Solid-State Drives (SSDs), optical diskdrives, caches, variations or combinations of one or more of the same,or any other suitable storage memory.

In some examples, the term “physical processor” generally refers to anytype or form of hardware-implemented processing unit capable ofinterpreting and/or executing computer-readable instructions. In oneexample, a physical processor may access and/or modify one or moremodules stored in the above-described memory device. Examples ofphysical processors include, without limitation, microprocessors,microcontrollers, Central Processing Units (CPUs), Field-ProgrammableGate Arrays (FPGAs) that implement softcore processors,Application-Specific Integrated Circuits (ASICs), portions of one ormore of the same, variations or combinations of one or more of the same,or any other suitable physical processor.

Although illustrated as separate elements, the modules described and/orillustrated herein may represent portions of a single module orapplication. In addition, in certain embodiments one or more of thesemodules may represent one or more software applications or programsthat, when executed by a computing device, may cause the computingdevice to perform one or more tasks. For example, one or more of themodules described and/or illustrated herein may represent modules storedand configured to run on one or more of the computing devices or systemsdescribed and/or illustrated herein. One or more of these modules mayalso represent all or portions of one or more special-purpose computersconfigured to perform one or more tasks.

In addition, one or more of the modules described herein may transformdata, physical devices, and/or representations of physical devices fromone form to another. Additionally or alternatively, one or more of themodules recited herein may transform a processor, volatile memory,non-volatile memory, and/or any other portion of a physical computingdevice from one form to another by executing on the computing device,storing data on the computing device, and/or otherwise interacting withthe computing device.

In some embodiments, the term “computer-readable medium” generallyrefers to any form of device, carrier, or medium capable of storing orcarrying computer-readable instructions. Examples of computer-readablemedia include, without limitation, transmission-type media, such ascarrier waves, and non-transitory-type media, such as magnetic-storagemedia (e.g., hard disk drives, tape drives, and floppy disks),optical-storage media (e.g., Compact Disks (CDs), Digital Video Disks(DVDs), and BLU-RAY disks), electronic-storage media (e.g., solid-statedrives and flash media), and other distribution systems.

The process parameters and sequence of the steps described and/orillustrated herein are given by way of example only and can be varied asdesired. For example, while the steps illustrated and/or describedherein may be shown or discussed in a particular order, these steps donot necessarily need to be performed in the order illustrated ordiscussed. The various exemplary methods described and/or illustratedherein may also omit one or more of the steps described or illustratedherein or include additional steps in addition to those disclosed.

The preceding description has been provided to enable others skilled inthe art to best utilize various aspects of the exemplary embodimentsdisclosed herein. This exemplary description is not intended to beexhaustive or to be limited to any precise form disclosed. Manymodifications and variations are possible without departing from thespirit and scope of the instant disclosure. The embodiments disclosedherein should be considered in all respects illustrative and notrestrictive. Reference should be made to the appended claims and theirequivalents in determining the scope of the instant disclosure.

Unless otherwise noted, the terms “connected to” and “coupled to” (andtheir derivatives), as used in the specification and claims, are to beconstrued as permitting both direct and indirect (i.e., via otherelements or components) connection. In addition, the terms “a” or “an,”as used in the specification and claims, are to be construed as meaning“at least one of.” Finally, for ease of use, the terms “including” and“having” (and their derivatives), as used in the specification andclaims, are interchangeable with and have the same meaning as the word“comprising.”

1. A computer-implemented method comprising: providing a visualizationthat depicts a representation of a vehicle and a representation of aphysical environment associated with the vehicle, the representationincluding an array of graphical elements that at least partiallysurrounds the vehicle; receiving, from at least one sensor of thevehicle depicted in the visualization, sensor data associated with thephysical environment; determining, based on the sensor data, that atleast one object occupies a position in the physical environmentrelative to the vehicle; altering at least a portion of thevisualization by dynamically changing one or more attributes of thegraphical elements that correspond to the position of the at least onedetected object based on one or more detection characteristics of thesensor data; and providing an updated visualization that illustrates thedynamically changed attributes of the graphical elements based on thedetection characteristics of the sensor data. 2-20. (canceled)
 21. Thecomputer-implemented method of claim 1, wherein the one or moredetection characteristics associated with the sensor data comprise anindication of whether the at least one detected object is above aspecified size.
 22. The computer-implemented method of claim 1, whereinthe one or more detection characteristics associated with the sensordata comprise an indication of whether the at least one detected objectis moving at a specified minimum speed.
 23. The computer-implementedmethod of claim 1, wherein the one or more detection characteristicsassociated with the sensor data comprise an indication of whether the atleast one detected object is an object of interest.
 24. Thecomputer-implemented method of claim 1, wherein the one or moredetection characteristics associated with the sensor data comprise anindication of an interruption in sensor data received from the vehicle.25. The computer-implemented method of claim 1, wherein dynamicallychanging one or more of the attributes of the graphical elements thatcorrespond to the position of the at least one detected object includeschanging a size of one or more of the graphical elements.
 26. Thecomputer-implemented method of claim 1, wherein dynamically changing oneor more of the attributes of the graphical elements that correspond tothe position of the at least one detected object includes changing ashape of one or more of the graphical elements.
 27. Thecomputer-implemented method of claim 1, wherein dynamically changing oneor more of the attributes of the graphical elements that correspond tothe position of the at least one detected object includes changing aposition of one or more of the graphical elements.
 28. Thecomputer-implemented method of claim 1, wherein dynamically changing oneor more of the attributes of the graphical elements that correspond tothe position of the at least one detected object includes changing acolor of one or more of the graphical elements.
 29. Thecomputer-implemented method of claim 1, further comprising dynamicallychanging the one or more attributes to an initial starting state upondetecting that the at least one detected object is no longer in thephysical environment represented by the array of graphical elements. 30.The computer-implemented method of claim 1, wherein dynamically changingthe one or more attributes of the graphical elements that correspond tothe position of the at least one detected object comprises applying asmoothing effect to the graphical elements that incrementally modifiesthe represented position of the at least one detected object.
 31. Thecomputer-implemented method of claim 30, wherein the smoothing effect isapplied based at least in part on an amount of time that the at leastone detected object has occupied the position.
 32. Thecomputer-implemented method of claim 1, wherein the at least onedetected object is outside of the representation of the physicalenvironment associated with the vehicle.
 33. The computer-implementedmethod of claim 1, wherein providing the visualization comprises:sending the visualization to a mobile device associated with thevehicle; and displaying the visualization on a display portion of themobile device associated with the vehicle.
 34. The computer-implementedmethod of claim 1, wherein providing the visualization comprises sendingthe visualization to a mobile computing device associated with apassenger within the vehicle.
 35. The computer-implemented method ofclaim 1, wherein the representation of the physical environmentassociated with the vehicle is depicted as being a predetermined radiusaway from a representation of the vehicle.
 36. A system comprising: anon-transitory memory; and one or more hardware processors configured toexecute instructions from the non-transitory memory to performoperations comprising: providing a visualization that depicts arepresentation of a vehicle and a representation of a physicalenvironment associated with the vehicle, the representation including anarray of graphical elements that at least partially surrounds thevehicle; receiving, from at least one sensor of the vehicle depicted inthe visualization, sensor data associated with the physical environment;determining, based on the sensor data, that at least one object occupiesa position in the physical environment relative to the vehicle; alteringat least a portion of the visualization by dynamically changing one ormore attributes of the graphical elements that correspond to theposition of the at least one detected object based on one or moredetection characteristics of the sensor data; and providing an updatedvisualization that illustrates the dynamically changed attributes of thegraphical elements based on the detection characteristics of the sensordata.
 37. The system of claim 36, wherein the one or more detectioncharacteristics associated with the sensor data comprise an indicationof whether the at least one detected object is above a specified size.38. The system of claim 36, wherein the one or more detectioncharacteristics associated with the sensor data comprise an indicationof whether the at least one detected object is moving at a specifiedminimum speed.
 39. At least one non-transitory computer-readable mediumcomprising: computer-readable instructions that, when executed by atleast one processor of a computing device, cause the computing deviceto: provide a visualization that depicts a representation of a vehicleand a representation of a physical environment associated with thevehicle, the representation including an array of graphical elementsthat at least partially surrounds the vehicle; receive, from at leastone sensor of the vehicle depicted in the visualization, sensor dataassociated with the physical environment; determine, based on the sensordata, that at least one object occupies a position in the physicalenvironment relative to the vehicle; alter at least a portion of thevisualization by dynamically changing one or more attributes of thegraphical elements that correspond to the position of the at least onedetected object based on one or more detection characteristics of thesensor data; and provide an updated visualization that illustrates thedynamically changed attributes of the graphical elements based on thedetection characteristics of the sensor data.